The generation of synthetic images of a three-dimensional scene is of importance in many applications e.g. in movie production, computer games, and computer-aided design and manufacturing processes. In particular, computer-implemented methods for rendering, storing, and/or reproducing an image of a three-dimensional scene, even of a simulated one, is of importance in many industrial applications, since e.g. they may be applied in investigating properties of an object and/or in designing an industrial article.
The prior art provides computer-implemented methods for image rendering, which comprise a process to sample the luminosity of the light impinging on the image plane and coming from the three-dimensional scene. Typically, the prior art of the present invention comprises either adaptive or non-adaptive sampling processes. Moreover the methods according to the prior art comprise a reconstruction process, which uses the luminosity samples generated by the sampling process to reconstruct the luminosity of the light impinging on the image plane.
The non-adaptive sampling processes of the prior art methods are based on a fixed, pre-determined sampling density, which does not take into account the actual features of the three-dimensional scene, and thus leads to a relatively inaccurate rendering of the image or require substantial computation time to obtain an image with few or new artefacts. Moreover, said sampling processes are typically based on the theory of Monte Carlo and/or Quasi Monte Carlo integration, which has been developed for integration problems and which was not conceived and only partially adapted to the problem of finding an approximated reconstruction of a continuous signal, e.g. of the luminosity of the light impinging on the image plane.
The adaptive sampling processes of the prior art methods, instead, are based on a sampling density that is successively refined by using the information about the image signal that is available at every stage of the computation. More specifically, said processes are able to determine the regions of the image plane requiring either a relatively high or a relatively low amount of samples and to adapt the sampling density accordingly. Such sampling processes, however, are not able to quantitatively determine the minimum number of samples said regions require, and thus they are not able to guarantee the reliability of the sample density they are based on.
The reconstruction processes of the prior art methods provide a representation of the luminosity of the light impinging on the image plane by assigning a luminosity and/or a colour value to each pixel of the given set of pixels composing the image. Said representation is obtained for a given rasterisation of the image, i.e. for a given number and/or a given location of the pixels rasterising the image. Therefore said representation is unsuitable for different rasterisations: if e.g. higher resolutions are needed, the sampling and the reconstruction procedures have to be repeated.
Moreover, the number of samples needed by said reconstruction processes is typically proportional to the number of pixels rasterising the image, number which scales steeply as the resolution of the image increase. This aspect strongly limits the resolution, which the images rendered by means of the prior art methods may attain.
Furthermore, the sampling process and the reconstruction process of the prior art methods are stand alone and are neither designed nor optimised to work together, e.g. the sampling density used in the former process is not chosen taking into account how the latter process performs the reconstruction. This strongly limits the efficiency and/or the reliability of the prior art methods.
Finally, the methods provided in the prior art do not allow for a quantitative assessment of the error associated to the reconstruction of the luminosity, for an estimation of the efficiency of said reconstruction, and/or for an evaluation of the computational effort needed to guarantee a given level of accuracy for the rendering of the image.